Method and system of crowd- sourced pedestrian localization

ABSTRACT

Methods and systems of broadcasting crowd-sourced localization data from a mobile device are described. The method, executed in the processor of the mobile device, comprises localizing the mobile device by determining an estimated position of the mobile device based on fingerprint data; broadcasting, at a first broadcast power level, a localization data packet that includes the estimated position to one or more peer mobile devices; and continuing the broadcasting at one of a lower broadcast power level than the first broadcast power level and a higher broadcast power level than the first broadcast power level based on a degree of accuracy of the estimated position.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority to U.S. application Ser.No. 15/928,167 filed Mar. 22, 2018, now issued as U.S. patent Ser. No.______, and hereby incorporates said U.S. application Ser. No.15/928,167 in the entirety herein.

TECHNICAL FIELD

The disclosure herein relates to the field of mobile device indoornavigation and localization.

BACKGROUND

Users of mobile devices are increasingly using and depending upon indoorpositioning and navigation applications and features. Seamless, accurateand dependable indoor positioning of a mobile device carried or worn bya user can be difficult to achieve using satellite-based navigationsystems when the latter becomes unavailable, or only sporadicallyavailable and therefore unreliable, such as within enclosed, orpartially enclosed, urban infrastructure and buildings, includinghospitals, shopping malls, airports, university campuses and industrialwarehouses. Pedestrian navigation or positioning solutions may rely onsensors including accelerometers, gyroscopes, and magnetometers that maybe commonly included in mobile phones and other mobile computingdevices, in conjunction with acquired wireless communication signal dataand magnetic field data to localize a pedestrian user in possession ofsuch a mobile device.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates, in an example embodiment, a crowd-sourced system forlocalization of a mobile device.

FIG. 2 illustrates, in one example embodiment, an architecture of amobile device used in a crowd-sourced localization system.

FIG. 3 illustrates an example embodiment of a localization data packetfor broadcast in a crowd-sourced localization system.

FIG. 4 illustrates an example embodiment of a method of localizing amobile device in a crowd-sourced localization system.

DETAILED DESCRIPTION

Among other benefits and technical effect, embodiments provided hereinallow one or more mobile devices that can be localized with a highdegree of accuracy to broadcast, via a wireless radio frequency (RF)signal, their estimated positions to peer mobile devices within a givenpedestrian area. The peer mobile devices that are generally in a knownproximate range, depending on transmission range inherent to a given RFsignal type, such as a Bluetooth Low Energy (BLE) signal type, from thebroadcasting mobile device may optionally use the broadcasted positiondata, as received, to establish their respective positions, or toconfirm their respective positions with an increased degree ofcertainty. More specifically, in such a crowd sourcing-based approach,users provided with, or carrying, an indoor positioning mobile device,may broadcast their known positions to other mobile devices within thecrowd of peer mobile devices. The method, executed in the processor ofthe mobile device, comprises localizing the mobile device by determiningan estimated position of the mobile device within the pedestrian areabased on accessing fingerprint data of the pedestrian area,broadcasting, at a first broadcast power level, a localization datapacket to one or more peer mobile devices within the pedestrian area,determining a confidence level indicative of a degree of accuracy forthe estimated position, then continuing the broadcasting at one of alower and a higher broadcast power level than the first broadcast powerlevel when it is determined that the confidence level is one of aboveand below a threshold confidence level.

In this manner, when the localization accuracy of a given broadcastingmobile device is high, then its broadcast RF power level is increased,resulting in a larger, more robust, broadcast range, especially as lessaccurately localized devices of the crowd of devices are relegated tobroadcasting at a lower RF signal power. Advantageously, as the lessaccurate mobile devices of the crowd of mobile devices are quiesced inbroadcasting power, those lower power RF signals propagating within thepedestrian area result in less signal interference to those mobiledevices broadcasting at the higher power level, creating conditions moreconducive to less noisy RF signal transmissions, which further helps toenhance localization accuracy of the latter devices. Moreover, by usingmore broadcast power for when positioning accuracy is relatively highand less power when positioning accuracy is lower, the reachability ofpositioning broadcast is enhanced whereby the position of a user withmore accuracy is favored for localization of other users.

The terms localize, or localization, as used herein refer to determininga unique coordinate position of the mobile device at a specific locationalong a pedestrian route being traversed relative to the indoor area orbuilding. In some embodiments, localization may also include determininga floor within the building, and thus involve determining not onlyhorizontal planar (x, y) coordinates, but also include a vertical, or z,coordinate of the mobile device, the latter embodying a floor numberwithin a multi-floor building, for example. In other embodiments, the(x, y, z) coordinates may be expressed either in a local reference framespecific to the mobile device, or in accordance with a global coordinatereference frame.

The pedestrian area, in embodiments, may be an indoor area within anyone of a shopping mall, a warehouse, an airport facility, a hospitalfacility, a university campus facility or any at least partiallyenclosed building. The term pedestrian as used herein is intended notencompass not only walking pedestrians, but also users of mobile phonesmoving at typical pedestrian speeds, for example at less than 10 milesper hour using automated means within the pedestrian area, including butnot limited to automated wheelchairs or automated people-moving indoorcarts.

A crowd-sourced system for localizing a mobile device within apedestrian area is also provided. The system comprises a first mobiledevice including a processor and a memory. The memory includesinstructions executable in the processor of the first mobile device tolocalize the mobile device by determining an estimated position of themobile device within the pedestrian area based on accessing fingerprintdata of the pedestrian area, broadcast, at a first broadcast powerlevel, a localization data packet within the pedestrian area, determinea confidence level indicative of a degree of accuracy for the estimatedposition, and continue the broadcast at one of a lower and a higherbroadcast power level than the first broadcast power level when it isdetermined that the confidence level is one of above and below athreshold confidence level. At least a second mobile device includes amemory and a processor, the memory including instructions executable inthe processor of the at least a second mobile device to, during thecontinuation of the broadcast, receive the localization data packet atthe at least a second mobile device, and localize the at least a secondmobile device using data of the localization data packet.

One or more embodiments described herein provide that methods,techniques, and actions performed by a computing device are performedprogrammatically, or as a computer-implemented method. Programmatically,as used herein, means through the use of code or computer-executableinstructions. These instructions can be stored in one or more memoryresources of the computing device. A programmatically performed step mayor may not be automatic.

One or more embodiments described herein can be implemented usingprogrammatic modules, engines, or components. A programmatic module,engine, or component can include a program, a sub-routine, a portion ofa program, or a software component or a hardware component capable ofperforming one or more stated tasks or functions. As used herein, amodule or component can exist on a hardware component independently ofother modules or components. Alternatively, a module or component can bea shared element or process of other modules, programs or machines.

A mobile device as described herein may be implemented, in whole or inpart, on mobile computing devices such as cellular or smartphones,laptop computers, wearable computer devices, and tablet devices. Memory,processing, and network resources may all be used in connection with theuse and performance of embodiments described herein, including with theperformance of any method or with the implementation of any system.

Furthermore, one or more embodiments described herein may be implementedthrough the use of logic instructions that are executable by one or moreprocessors. These instructions may be carried on a computer-readablemedium. In particular, machines shown with embodiments herein includeprocessor(s) and various forms of memory for storing data andinstructions. Examples of computer-readable mediums and computer storagemediums include portable memory storage units, and flash memory (such ascarried on smartphones). A mobile device as described herein utilizesprocessors, memory, and logic instructions stored on computer-readablemedium. Embodiments described herein may be implemented in the form ofcomputer processor-executable logic instructions or programs stored oncomputer memory mediums.

System Description

FIG. 1 illustrates, in an example embodiment, crowd-sourced system 100for localization of any of mobile devices 101 a-n within a pedestrianarea. Mobile devices 101 a-n may be such as a cellular or smartphone, alaptop or a tablet computer, or a wearable computer device that may beoperational for any one or more of telephony, data communication, anddata computing. As used herein, designation as mobile device 101 mayrefer to any one of collective mobile devices 101 a-n. Mobile device 101may include fingerprint data of a surrounding or proximate pedestrianarea stored in local memory. In other variations, mobile device 101 maybe connected within a computer network communication system, includingthe internet or other wide area network, to one or more remote servercomputing device(s) storing the fingerprint data of the pedestrian area,the latter being communicatively accessible to mobile device 101 fordownload of the fingerprint data.

A pedestrian navigation, or indoor positioning, software applicationdownloaded and installed, or stored, in a memory of mobile device 101may render physical layout map of a facility or building of a pedestrianarea within a user interface display of mobile device 101. In oneembodiment, the pedestrian navigation software application mayincorporate one or more portions of processor-executable instructionsmanifesting crowd-sourced localization logic module 105. The termslocalize, or localization, as used herein means to determine anestimated coordinate position (x, y, z) along a pedestrian route ortrajectory being traversed in accompaniment of mobile device 101,ideally with an indoor positioning accuracy of up to one meter orbetter. The display of physical layout map may further show a trajectoryor pedestrian route traversed by a user in possession of mobile device101 within the pedestrian area.

Mobile device 101 may include sensor functionality by way of sensordevices. The sensor devices may include inertial sensors such as anaccelerometer and a gyroscope, and magnetometer or other magnetic fieldsensing functionality, barometric or other ambient pressure sensingfunctionality, humidity sensor, thermometer, and ambient lightingsensors such as to detect ambient lighting intensity. Mobile device 101may also include capability for detecting and communicatively accessingambient wireless communication signals including but not limited to anyof Bluetooth and Bluetooth Low Energy (BLE), Wi-Fi, RFID, and alsosatellite-based navigations signals including global positioning system(GPS) signals. Mobile device 101 further includes the capability fordetecting, via sensor devices, and measuring a received signal strength,and of determining signal connectivity parameters, related to theambient wireless signals. In particular, mobile device 101 may includelocation determination capability such as by way of a GPS module havinga GPS receiver, and a communication interface for communicativelycoupling to communication network 107, including by sending andreceiving cellular data over data and voice channels.

A fingerprint data repository, or any portion(s) thereof, may be storedin a remote computing server device and made communicatively accessibleto mobile device 101 via a communication network. In some embodiments,it is contemplated that the fingerprint data repository, or any portionsof data and processor-executable instructions constituting thefingerprint data repository, may be downloaded for storage, at leasttemporarily, within a memory of mobile device 101. In embodiments, thefingerprint map data stored in the fingerprint data repository furtherassociates particular positions along a pedestrian route of an indoorfacility with any combination of fingerprint data, including gyroscopedata, accelerometer data, wireless signal strength data, wirelessconnectivity data, magnetic data, barometric data, acoustic data,line-of sight data, and ambient lighting data stored thereon.

The terms fingerprint and fingerprint data as used herein refer totime-correlated, individual measurements of any of, or any combinationof, received wireless communication signal strength and signalconnectivity parameters, magnetic field parameters (strength, direction)or barometric pressure parameters, and mobile device inertial sensordata at known, particular locations along a route being traversed, andalso anticipated for traversal, by the mobile device. In other words, afingerprint as referred to herein may include a correlation of sensorand signal information (including, but not necessarily limited towireless signal strength, wireless connectivity information, magnetic orbarometric information, inertial sensor information and GPS locationinformation) associated for a unique location relative to the facility.Thus, fingerprint data associated with a particular location or positionmay provide a fingerprint signature that uniquely correlates to thatparticular location or position. A sequence of positions or locationsthat constitute a navigation path traversed by the mobile devicerelative to a given indoor facility may be fingerprint-mapped during acalibration process, and the resulting fingerprint map stored in thefingerprint data repository. Fingerprint maps of a respective buildingor indoor facility, or any portions thereof, may be downloaded into amemory of mobile device 101 for use in conjunction with the pedestriannavigation software application executing thereon.

A particular fingerprint or signature based on any of received wirelesscommunication signal strength and signal connectivity parameters,magnetic field parameters or barometric pressure parameters, and mobiledevice inertial sensor data may be detected or recorded by mobile device101, whereupon the fingerprint or signature as detected may be matchedto a reference fingerprint, or a reference pattern including a set offingerprints, in a stored fingerprint map of a given facility madeaccessible to crowd-sourced localization logic module 105 to identify aunique position of mobile device 101 along a pedestrian route. As usedherein, term signal connectivity, as distinguished from signal strength,refers to a wireless radio frequency (RF) signal being available for usein bi-directional data communication, such as between devices that bothtransmit and receive data using that available wireless RF signal. Insome embodiments, given that sampling times and sampling rates appliedin conjunction with particular mobile device sensors may be different,the signal and sensor information as measured during the fingerprintcalibration process may be time-averaged across particular periods oftime, with the time-averaged value being used to represent the signalinformation at any given instance of time within that particular periodof time in which the signal information is time-averaged. Fingerprintdata may be used to track traversal of mobile device 101 along asequence of positions that constitute pedestrian route within, and evenadjoining, the indoor facility.

Crowd-sourced localization logic module 105, constituted of logicinstructions executable in a processor of mobile device 101 in oneembodiment, may be hosted at mobile device 101 and provides, at least inpart, capability for system localizing a mobile device along apedestrian route traversed in an indoor area or facility. In alternateembodiments, one or more portions constituting crowd-sourcedlocalization logic module 105 may be hosted remotely at a server deviceand made communicatively accessible to mobile device 101 viacommunication network 107.

FIG. 2 illustrates, in one example embodiment, an architecture of mobiledevice 101 used in crowd-sourced localization system 100. Mobile device101 may include processor 201, memory 202, display screen 203, inputmechanisms 204 such as a keyboard or software-implemented touchscreeninput functionality, barcode, QR code or other symbol- or code-scannerinput functionality. Memory 202 of mobile device 101 may be constitutedof a random access memory, such as a dynamic or a static random accessmemory, in some embodiments configured or partitioned into memoryportions.

Mobile device 101 may include sensor functionality by way of sensordevices 205. Sensor devices 205 may include inertial sensors such as anaccelerometer and a gyroscope, and magnetometer or other magnetic fieldsensing functionality, barometric or other ambient pressure sensingfunctionality, and ambient lighting sensors, such as to detect ambientlighting intensity. Mobile devices 101 a-n may also include capabilityfor both transmitting and detecting, ambient wireless communicationsignals including but not limited to any of Bluetooth and Bluetooth LowEnergy (BLE), Wi-Fi, RFID, and also satellite-based navigations signalssuch as, but not limited to, global positioning system (GPS) signals.For example, a BLE signal packet may typically include identifiers thatare advertised publicly, whereby any other mobile device that can detectthe BLE signals as transmitted or broadcast will be able to capturethese identifiers. Mobile device 101 may, in this manner, be equippedwith, and include capability for detecting, via sensor devices 205, andmeasuring a received signal strength, and for determining signalconnectivity parameters, related to the ambient wireless radio frequency(RF) signals.

Mobile device 101 may also include location or position determinationcapability such as by way of GPS module 206 having a GPS receiver, andcommunication interface 207 for communicatively coupling tocommunication network 107, including by sending and receiving cellularand other RF data over data and voice channels.

Crowd-sourced localization logic module 105 of mobile device 101includes instructions stored in memory 202 of mobile device 101,executable in processor 201. Crowd-sourced localization logic module 105may comprise sub-modules, or portions thereof, including positionestimation module 210, localization packet broadcast module 211,localization accuracy estimator module 212 and broadcast power adjustormodule 213. In alternate embodiments, it is contemplated that any one ormore, or portions, of sub-modules including position estimation module210, localization packet broadcast module 211 and localization accuracyestimator module 212 may be located at remote server devicecommunicatively accessible to mobile device 101 via networkcommunication interface 207.

Processor 201 uses executable instructions of position estimation module210 to localize mobile device 101 to localize mobile device 101 to anestimated first position within the pedestrian area based on accessingfingerprint data of the pedestrian area.

Processor 201 uses executable instructions stored in localization packetbroadcast module 211 to broadcast, or transmit, at a first broadcastpower level, a localization data packet that includes data of theestimated position to one or more peer mobile devices within thepedestrian area. The broadcasting power level may be measured in dBM(Decibel-milliwatts) and the more, or higher, the broadcast power level,the further the BLE signal is broadcast.

Processor 201 uses executable instructions stored in localizationaccuracy estimator module 212 to determine a confidence level indicativeof a degree of accuracy for the estimated position. Since the accuracyassociated with estimating the position, or location, of a mobile device101 as a consequence of localization is not absolute, but rather issubject to the statistical, or probabilistic, nature of the fingerprintparameters, including but not limited to the inherently probabilisticnature of wireless radio frequency signal parameters as received.

Processor 201 uses executable instructions stored in broadcast poweradjustor module 213 to continue broadcasting of the localization datapacket at one of a lower and a higher broadcast power level when it isdetermined that the confidence level is one of above and below athreshold confidence level. In this manner, when the localizationaccuracy of mobile device 101 is high, then the broadcast power level isincreased, so mobile device 101 may broadcast its localized position,encoded in the localization data packet being broadcast, to peer mobiledevices 101 a-n within the pedestrian area. Conversely, andbeneficially, if the accuracy estimated position of mobile device 101 aslocalized is determined to be low, then mobile device 101 may be“quieted”, or quiesced, via executable instructions stored in broadcastpower adjustor module 213 by lowering its broadcasting power level. Inaggregate regard to the peer mobile devices 101 a-n within thepedestrian area, those one or more mobile devices having localized orestimated positions with the highest accuracy are accorded the “loudest”broadcast, at the expense of the less accurately localized mobiledevices of that set of peer devices 101 a-n. Advantageously, as the lessaccurate mobile devices 101 a-n are quiesced in broadcasting power, thelower power RF signals propagating within the pedestrian area result inless signal interference to those mobile devices broadcasting at thehigher power level, creating a less noisy signal transmissionenvironment that enhances the accuracy of localization for the higherpower broadcasting mobile devices. Moreover, by using more broadcastpower for when positioning accuracy is relatively high and less powerwhen positioning accuracy is lower, the reachability of positioningbroadcast is enhanced whereby the position of a user with more accuracyis favored for localization of other users—for example, throughtriangulation or linear/nonlinear least square or other positionestimation techniques.

In additional variations, at least a second mobile device in the set ofpeer mobile devices 101 a-n within the pedestrian area receives thelocalization data packet broadcast by mobile device 101, and copies thelocalized position including floor information encoded with thelocalization data packet to establish its own position. In anotherembodiment, the second mobile device independently performs itslocalization to establish its position, but then uses the localizationinformation as received from mobile device 101 to confirm its positionas localized.

Methodology

FIG. 3 illustrates an example embodiment of a localization data packet300 for broadcast in a crowd-sourced localization system 100. Indescribing examples of FIG. 3, reference is made to the examples ofFIGS. 1-2 for purposes of illustration.

Localization data packet 300 may include location (x, y) coordinateinformation and floor number information 304 of mobile device 101, suchas for a multi-floor building constituting the pedestrian area. Thelocalization data packet may further be partitioned to include preamblecomponent 300. Preamble component 300 may include a company identifieror other identifier associated with either a proprietary or a standardformatting of the localization data packet, based upon which, forexample, the information encoded in localization data packet may becorrectly decoded into specific (x, y, z) coordinates to establish aposition of mobile device 101 as localized within the pedestrian area.In yet another variation, localization data packet may be constitutedwith a total of 20 bytes of information, of which preamble component 300may be constituted of 8 bytes, and x-coordinate 302, y-coordinate 303and floor number 304 each constituted of 4 bytes. In a furthervariation, the localization data packet is broadcast from mobile device101 via a BLE advertising mode.

FIG. 4 illustrates an example embodiment of method 400 of localizingmobile device 101 within crowd-sourced localization system 100. Indescribing examples of FIG. 4, reference is made to the examples ofFIGS. 1-3 for purposes of illustrating suitable components or elementsfor performing a step or sub-step being described.

Examples of method steps described herein relate to the use of mobiledevice 101 for implementing the techniques described. According to oneembodiment, the techniques are performed by crowd-sourced localizationlogic module 105 of mobile device 101 in response to the processor 201executing one or more sequences of software logic instructions thatconstitute crowd-sourced localization logic module 105. In embodiments,crowd-sourced localization logic module 105 may include the one or moresequences of instructions within sub-modules including positionestimation module 210, localization packet broadcast module 211 andlocalization accuracy estimator module 212, and broadcast power adjustormodule 213. Such instructions may be read into memory 202 frommachine-readable medium, such as memory storage devices, or downloadedinto memory 202 via network communication interface 207. In executingthe sequences of instructions of position estimation module 210,localization packet broadcast module 211, localization accuracyestimator module 212 and broadcast power adjustor module 213 ofcrowd-sourced localization logic module 105 in memory 202, processor 201performs the process steps described herein. In alternativeimplementations, at least some hard-wired circuitry may be used in placeof, or in combination with, the software logic instructions to implementexamples described herein. Thus, the examples described herein are notlimited to any particular combination of hardware circuitry and softwareinstructions. Additionally, it is contemplated that in alternativeembodiments, the techniques herein, or portions thereof, may bedistributed between mobile device 101 and a remote but communicativelyaccessible server computing device.

At step 410, processor 201 executes instructions included in positionestimation module 210 to localize mobile device 101 to an estimatedfirst position within the pedestrian area based on accessing fingerprintdata of the pedestrian area.

As would be appreciated by those of skill in the art, any localization,or location determination, of mobile device 101 that is based, even atleast partly, on fingerprint data constituted of radio frequency (RF)signal parameters as broadcast, cannot be guaranteed with absolutecertainty. For instance, considering a Bluetooth Low Energy (BLE)context for illustration purposes, such a BLE signal as broadcast in the2.4 GHz radio frequency may be may be distorted and attenuated byinterference from specific elements in the environment of the pedestrianarea. Such signal interfering elements may include metallic surfacesbouncing the BLE signal off the surface in unexpected ways as it isunable to penetrate the material, BLE signal absorption, attenuation anddistortion caused by human body mass absorbing, water, concrete, marbleand brick structures and distorting BLE signal, other mobile devices 101a-n and other electronic devices operating in the 2.4 GHz frequency,fluorescent lighting emitting signals in the 2.4 GHz frequency, andelectric power lines, for example. When the BLE signal is distorted, themobile device will receive a signal that does not reflect the realsituation, e.g. the distance to a fingerprint data point or positionmight not be accurate, since the accuracy levels are affected by varioussources of signal distortion and might not reflect the actual (x, y)coordinate determined by localization.

At step 420, processor 201 executes instructions included inlocalization packet broadcast module 211 to broadcast, or transmit, at afirst broadcast power level, a localization data packet that includesdata of the estimated position to one or more peer mobile devices withinthe pedestrian area. The broadcasting power level may be measured in dBM(Decibel-milliwatts) and the more, or higher, the broadcast power level,the further the BLE signal is broadcast.

In one embodiment, localization data packet 300 includes location (x, y)coordinate information and floor number information 304 of mobile device101, such as for a multi-floor building constituting the pedestrianarea. The localization data packet may further be partitioned to includepreamble component 300. Preamble component 300 may include a companyidentifier or other identifier associated with either a proprietary or astandard formatting of the localization data packet, based upon which,for example, the information encoded in localization data packet may becorrectly decoded into specific (x, y, z) coordinates to establish aposition of mobile device 101 as localized within the pedestrian area.

At step 430, processor 201 executes instructions included inlocalization accuracy estimator module 212 to, determining a confidencelevel indicative of a degree of accuracy for the estimated position.Since the accuracy associated with estimating the position, or location,of a mobile device 101 as a consequence of localization is not absolute,but rather is subject to the statistical, or probabilistic, nature ofthe fingerprint parameters, including but not limited to the inherentlyprobabilistic nature of wireless radio frequency signal parameters asreceived. In some embodiments, a degree of accuracy associated with theposition estimation may be indicated by a confidence level that isdetermined for, and assigned in conjunction with, estimated first andsecond positions 301, 311 as localized. As a measure of the accuracy oflocalization of mobile device 101, the confidence level associated withthe location estimate may be obtained by fusing the probabilisticresults of multiple concurrent location estimates. In some embodiments,the variance in the x and y components, with respect to their meanvalues (μ_(x), μ_(y)), can be estimated independently as:

$\sigma_{x}^{2} = {\frac{1}{N - 1}{\sum\left( {x - \mu_{x}} \right)^{2}}}$$\sigma_{y}^{2} = {\frac{1}{N - 1}{\sum\left( {y - \mu_{y}} \right)^{2}}}$

and combined to produce the confidence level. In one embodiment, theoverall confidence level can be selected as a function of the maximumstandard deviation of the x-y components, as σ=max(σ_(x), σ_(y)). Inother embodiments, a weighted variance of the x and y, where the weightsare based on the probability of each individual estimate can be used toproduce the confidence estimate. When multiple trajectory-based locationestimates are available, trajectories can be grouped into categoriesbased on similarity and a probability spread/confidence can be assignedon a per-group basis. If the per-group probability/confidence level ofone group significantly exceeds that of the other groups, then theconfidence in the validity of that group is raised, and hence, theconfidence in the location estimate increases. Conversely, if severaldistinct per-group probabilities are similar, then the confidence in theper-group results are reduced, leading to a lower confidence level. Thusthe estimated position comprises a probabilistic estimate expressed as aconfidence level. In one embodiment, the threshold confidence level maybe established using a range of from 60 to 90 percent.

Generally, in context of one or more mobile devices 101 a-n collectivelybroadcasting and receiving broadcast BLE signals, as the variability inthe BLE signals existing in the pedestrian area decreases, referred toherein as a tighter or narrower normal distribution of signalparameters, such as due to lowered levels of signal interference thatresult from lower broadcast power levels, the confidence levelassociated with the accuracy of estimated position of mobile device 101increases.

At step 440, processor 201 executes instructions included in broadcastpower adjustor module 213 to continue broadcasting, by mobile device101, at one of a lower and a higher broadcast power level if theconfidence level is one of above and below a threshold confidence level.

In some embodiments, the lower and higher broadcast power levels mayrange from −100 to +20 Decibel-milliwatts (dBm), representing the BLEsignal strength as measured by mobile devices 101 a-n.

It is contemplated for embodiments described herein to extend toindividual elements and concepts described herein, independently ofother concepts, ideas or system, as well as for embodiments to includecombinations of elements recited anywhere in this application. Althoughembodiments are described in detail herein with reference to theaccompanying drawings, it is to be understood that the invention is notlimited to those precise embodiments. As such, many modifications andvariations will be apparent to practitioners skilled in this art.Accordingly, it is intended that the scope of the invention be definedby the following claims and their equivalents. Furthermore, it iscontemplated that a particular feature described either individually oras part of an embodiment can be combined with other individuallydescribed features, or parts of other embodiments, even if the otherfeatures and embodiments make no specific mention of the particularcombination of features. Thus, the absence of describing combinationsshould not preclude the inventors from claiming rights to suchcombinations.

1. A method of broadcasting crowd-sourced localization data from amobile device, the mobile device including a processor and a memory, themethod executed in the processor and comprising: broadcasting, at afirst broadcast power level, a localization data packet that includes anestimated position of the mobile device to one or more peer mobiledevices; and continuing the broadcasting at one of a lower broadcastpower level than the first broadcast power level and a higher broadcastpower level than the first broadcast power level based on a degree ofaccuracy of the estimated position, the degree of accuracy being definedby a confidence level determined by fusing probabilistic results of oneor more concurrent location estimates of the mobile device. 2.(canceled)
 3. The method of claim 1, wherein continuing the broadcastingcomprises lowering the first broadcast power level, when it isdetermined that the confidence level is below a threshold confidencelevel and increasing the first broadcast power level when it isdetermined that the confidence level is above the threshold confidencelevel.
 4. The method of claim 2 wherein the threshold confidence levelindicates 60 to 90 percent probability of the estimated position beingaccurate.
 5. The method of claim 2 wherein the lower and higherbroadcast power levels range from −100 to +20 Decibel-milliwatts (dBm).6. The method of claim 1 wherein the localization data packet compriseslocation coordinate information and floor number information within apedestrian area.
 7. The method of claim 1 wherein the localization datapacket further comprises a preamble component, the preamble componentincluding a company identifier associated with formatting of thelocalization data packet.
 8. The method of claim 1 wherein thelocalizing is based on at least one of an orientation, a magnetic fieldstrength and direction, a received wireless communication signalstrength, a wireless connectivity indication and a barometric pressurein accordance with fingerprint data.
 9. The method of claim 8 whereinthe fingerprint data includes respective time-stamps whereby theorientation, the magnetic field strength and direction, the receivedwireless signal strength, the wireless connectivity indication and thebarometric pressure are correlated in accordance with the respectivetime-stamps.
 10. The method of claim 1 wherein the estimated positioncomprises a probabilistic estimate expressed as the confidence level.11. A crowd-sourced system for localizing a mobile device within apedestrian area, the system comprising: a first mobile device includinga processor and a memory, the memory including instructions executablein the processor of the first mobile device to: broadcast, at a firstbroadcast power level, a localization data packet that includes anestimated position of the first mobile device to one or more peer mobiledevices; and continue the broadcasting at one of a lower broadcast powerlevel than the first broadcast power level and a higher broadcast powerlevel than the first broadcast power level based on a degree of accuracyof the estimated position, the degree of accuracy being defined by aconfidence level determined by fusing probabilistic results of one ormore concurrent location estimates of the first mobile device; and asecond mobile device including a memory and a processor, the memoryincluding instructions executable in the processor of the second mobiledevice to: during the continuation of the broadcast, receive thelocalization data packet at the second mobile device; and localize thesecond mobile device using the estimated position in the localizationdata packet corresponding to the first mobile device.
 12. (canceled) 13.The crowd-sourced system of claim 11 continuing the broadcastingcomprises lowering the first broadcast power level, when it isdetermined that the confidence level is below a threshold confidencelevel and increasing the first broadcast power level when it isdetermined that the confidence level is above the threshold confidencelevel.
 14. The crowd-sourced system of claim 12 wherein the thresholdconfidence level indicates 60 to 90 percent probability of the estimatedposition being accurate.
 15. The crowd-sourced system of claim 12wherein the lower and higher broadcast power levels range from −100 to+20 Decibel-milliwatts (dBm).
 16. The crowd-sourced system of claim 11wherein the localization data packet comprises location coordinateinformation and floor number information within a pedestrian area. 17.The crowd-sourced system of claim 11 wherein the localization datapacket further comprises a preamble component, the preamble componentincluding a company identifier associated with formatting of thelocalization data packet.
 18. The crowd-sourced system of claim 11wherein the localizing is based on at least one of an orientation, amagnetic field strength and direction, a received wireless communicationsignal strength, a wireless connectivity indication and a barometricpressure in accordance with fingerprint data.
 19. The crowd-sourcedsystem of claim 11 wherein the fingerprint data includes respectivetime-stamps whereby the orientation, the magnetic field strength anddirection, the received wireless signal strength, the wirelessconnectivity indication and the barometric pressure are correlated inaccordance with the respective time-stamps.
 20. The crowd-sourced systemof claim 11 wherein the estimated position comprises a probabilisticestimate expressed as the confidence level.
 21. The method of claim 1,wherein the method comprises localizing the mobile device by determiningthe estimated position of the mobile device based on fingerprint data.22. The crowd-sourced system of claim 11, wherein the processor of thefirst mobile device is to localize the first mobile device bydetermining the estimated position of the mobile device based onfingerprint data.